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Zero-Shot Text Classifier via LLM
x402
Zero-Shot Text Classifier via LLM is a paid API for AI agents from ai-policy-kit.moneymaker-ai.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-28).
Classifies a text snippet into one of your provided labels using zero-shot LLM inference powered by IBM Granite on Cloudflare Workers AI
Price / call$0.002
Success—
StatusUnknown
How this service works
Generate an AI_POLICY.md and a pull request template with an AI-disclosure section for your open-source repo. Free, runs in your browser.
Output
Returns a JSON object with the predicted label, a human-readable reason for the classification, the LLM engine used, the list of allowed labels, whether multi-label mode was active, an estimated neuron cost, and a disclaimer that the score is a zero-shot LLM output without calibrated probability.
Example use cases
Customer support ticket triage
“Take this incoming support ticket and classify it as billing, shipping, technical, account, or other so I know which team to route it to: 'My package was marked delivered but I never received it.'”
Feedback theme labeling
“I have this piece of user feedback: 'The login page keeps crashing on mobile.' Label it as one of these categories — bug, feature-request, performance, UX, or other — and tell me why you chose that label.”
Content moderation routing
“Classify this user-submitted comment as safe, spam, hate-speech, or off-topic, and tell me your reasoning: 'Click here to win a free iPhone — limited time offer!!!'”
Technical details
Endpoint
GEThttps://ai-policy-kit.moneymaker-ai.workers.dev/api/ai/classify?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need lightweight, zero-shot text classification against a custom label set without training a model. It is ideal for routing support tickets, labeling feedback, or tagging content in real time when you already know your label taxonomy. Prefer it over fine-tuned classifiers when speed of setup matters more than calibrated probability scores, and over general LLM chat calls when you want a structured JSON response with the label and reasoning included.
Side effects
- Too few or too many labels (outside 2-50 range) returns a validation error
- Text exceeding 8,000 characters may be truncated or rejected
- Ambiguous text with no clear label match may produce unreliable or low-confidence results
- Payment not attached or insufficient USDC (0.002) results in 402 response
- Labels array malformed (not valid JSON array or comma-separated) causes a parse error
- Network timeout on Cloudflare Workers AI backend returns 5xx
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"note": "Zero-shot LLM label; no calibrated probability. Validate on a sample of your own data before relying on it.",
"label": "billing",
"engine": "@cf/ibm-granite/granite-4.0-h-micro on Cloudflare Workers AI",
"reason": "The text discusses an issue with a payment being charged twice for the same order, which falls under the billing category.",
"multi_label": false,
"allowed_labels": [
"billing",
"shipping",
"technical",
"account",
"other"
],
"neurons_estimated": 0.52
}
}Tags
x402
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